{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "default_stdout = sys.stdout\n",
    "default_stderr = sys.stderr\n",
    "reload(sys)\n",
    "sys.setdefaultencoding(\"utf-8\")\n",
    "sys.stdout = default_stdout\n",
    "sys.stderr = default_stderr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys \n",
    "from os import getcwd, path\n",
    "sys.path.append(path.dirname(getcwd()))\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from utils import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dataframe_hash': -9059328030452674214,\n",
      " 'provenance_file_summary': {u'cohorts': u'0.4.0+3.gda968fb',\n",
      "                             u'isovar': u'0.0.6',\n",
      "                             u'mhctools': u'0.3.0',\n",
      "                             u'numpy': u'1.11.1',\n",
      "                             u'pandas': u'0.18.1',\n",
      "                             u'pyensembl': u'1.0.3',\n",
      "                             u'scipy': u'0.18.1',\n",
      "                             u'topiary': u'0.1.0',\n",
      "                             u'varcode': u'0.5.10'}}\n"
     ]
    }
   ],
   "source": [
    "cohort = data.init_cohort(exclude_patient_ids=set(),\n",
    "                          only_patients_with_bams=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mo_3_boolean_label = \"Overall Survival\"\n",
    "mo_3_boolean_value_map = {True: \"> 3 Months\", False: \"≤ 3 Months\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dataframe_hash': 9021847847770784199,\n",
      " 'provenance_file_summary': {u'cohorts': u'0.4.0+3.gda968fb',\n",
      "                             u'isovar': u'0.0.6',\n",
      "                             u'mhctools': u'0.3.0',\n",
      "                             u'numpy': u'1.11.1',\n",
      "                             u'pandas': u'0.18.1',\n",
      "                             u'pyensembl': u'1.0.3',\n",
      "                             u'scipy': u'0.18.1',\n",
      "                             u'topiary': u'0.1.0',\n",
      "                             u'varcode': u'0.5.10'}}\n"
     ]
    }
   ],
   "source": [
    "df = cohort.as_dataframe()\n",
    "less_3_mos_ids = set(df[~df.is_late_deceased].patient_id)\n",
    "cohort_greater_3_mos = data.init_cohort(\n",
    "    only_patients_with_bams=False, exclude_patient_ids=less_3_mos_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cohorts.functions import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Clinical factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def late_deceased(row):\n",
    "    return row[\"patient_id\"] not in less_3_mos_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from utils.paper import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liver Met         False  True \n",
      "Overall Survival              \n",
      "> 3 Months           18      5\n",
      "≤ 3 Months            0      6\n",
      "Fisher's Exact Test: OR: inf, p-value=0.000972590627763 (two-sided)\n",
      "{{{late_deceased_liver_plot}}}\n",
      "{{{late_deceased_liver_late_deceased:22%}}}\n",
      "{{{late_deceased_liver_no_late_deceased:100%}}}\n",
      "{{{late_deceased_liver_fishers:n=29, Fisher's Exact p=0.00097}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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cOVKvRRIRkXGSfSSyYMEC2NnZ6UyMqFKpMH/+fHh4eOi7TiIiMkKyl8ft27cvfH19cerU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\nGEOEiIgUY4gQEZFi/w9oxLuUhKefUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7facf0918d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fishers_exact_hyper_label_printer(cohort.plot_boolean(on={\"Liver Met\": lambda row: row[\"Liver Mets\"] == 1, \"> 3 Months OS\": late_deceased},\n",
    "                    plot_col=\"Liver Met\",\n",
    "                    boolean_label=mo_3_boolean_label,\n",
    "                    boolean_value_map=mo_3_boolean_value_map,\n",
    "                    boolean_col=\"> 3 Months OS\"), \"late_deceased_liver\", split_col=\"late_deceased\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing 5-Factor Score for 3 patients: from 29 to 26\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney test: U=22.0, p-value=0.0177410474356 (two-sided)\n",
      "{{{late_deceased_five_factor_plot}}}\n",
      "{{{late_deceased_five_factor_late_deceased:1.50 (range 0.00-4.00)}}}\n",
      "{{{late_deceased_five_factor_no_late_deceased:3.00 (range 2.00-4.00)}}}\n",
      "{{{late_deceased_five_factor_mw:n=26, Mann-Whitney p=0.018}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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8eVIfZRAREXiOgIhI9vRyREBElP/kMXJT4yAYmwKFhXj66F8AgJljI5jVaojM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\nxyAgIpK5/w/fxbbL8ZUwVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad099ef710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mann_whitney_hyper_label_printer(cohort.plot_boolean(on={\"5-Factor Score\": lambda row: row[\"5-factor score\"], \"> 3 Months OS\": late_deceased},\n",
    "                    plot_col=\"5-Factor Score\",\n",
    "                    boolean_label=mo_3_boolean_label,\n",
    "                    boolean_value_map=mo_3_boolean_value_map,\n",
    "                    boolean_col=\"> 3 Months OS\"), label=\"late_deceased_five_factor\", split_col=\"late_deceased\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prior BCG         False  True \n",
      "Overall Survival              \n",
      "> 3 Months           15      8\n",
      "≤ 3 Months            2      4\n",
      "Fisher's Exact Test: OR: 3.75, p-value=0.198063239443 (two-sided)\n",
      "{{{late_deceased_bcg_plot}}}\n",
      "{{{late_deceased_bcg_late_deceased:35%}}}\n",
      "{{{late_deceased_bcg_no_late_deceased:67%}}}\n",
      "{{{late_deceased_bcg_fishers:n=29, Fisher's Exact p=0.20}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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wAenp6QAg3fIrR61atRAfH4/mzZtj0qRJmDhxIlxcXLB69WqdI5qKPBVf0WFX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\niBRjiBARkWIMESIiUowhQkREiv0/Cde21ygbJUwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7facee37ed10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fishers_exact_hyper_label_printer(cohort.plot_boolean(on={\"Prior BCG\": lambda row: row[\"Prior BCG\"] == \"Y\", \"> 3 Months OS\": late_deceased},\n",
    "                    plot_col=\"Prior BCG\",\n",
    "                    boolean_label=mo_3_boolean_label,\n",
    "                    boolean_value_map=mo_3_boolean_value_map,\n",
    "                    boolean_col=\"> 3 Months OS\"), label=\"late_deceased_bcg\", split_col=\"late_deceased\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with tcr_peripheral_a: 29 to 29 rows\n",
      "inner join with tcr_peripheral_a: 29 to 29 rows\n",
      "{'dataframe_hash': -8062678075537494193,\n",
      " 'provenance_file_summary': {u'cohorts': u'0.4.0+3.gda968fb',\n",
      "                             u'isovar': u'0.0.6',\n",
      "                             u'mhctools': u'0.3.0',\n",
      "                             u'numpy': u'1.11.1',\n",
      "                             u'pandas': u'0.18.1',\n",
      "                             u'pyensembl': u'1.0.3',\n",
      "                             u'scipy': u'0.18.1',\n",
      "                             u'topiary': u'0.1.0',\n",
      "                             u'varcode': u'0.5.10'}}\n"
     ]
    }
   ],
   "source": [
    "cohort = data.init_cohort(exclude_patient_ids=set(),\n",
    "                          only_patients_with_bams=False,\n",
    "                          join_with=\"tcr_peripheral_a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with tcr_peripheral_a: 29 to 29 rows\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney test: U=40.0, p-value=0.124934124517 (two-sided)\n",
      "{{{late_deceased_tcr_plot}}}\n",
      "{{{late_deceased_tcr_late_deceased:0.068 (range 0.022-0.35)}}}\n",
      "{{{late_deceased_tcr_no_late_deceased:0.15 (range 0.082-0.22)}}}\n",
      "{{{late_deceased_tcr_mw:n=29, Mann-Whitney p=0.12}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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vb4/79+9LroGI6jYLCwvY2toiNDQUb7zxBlq3bg25XI7Zs2fDwcHB0OXVKZLO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7facf08a8a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mann_whitney_hyper_label_printer(cohort.plot_boolean(on={\"TCR Peripheral A Clonality\": lambda row: row[\"Clonality\"], \"> 3 Months OS\": late_deceased},\n",
    "                    plot_col=\"TCR Peripheral A Clonality\",\n",
    "                    boolean_label=mo_3_boolean_label,\n",
    "                    boolean_value_map=mo_3_boolean_value_map,\n",
    "                    boolean_col=\"> 3 Months OS\"), \"late_deceased_tcr\", split_col=\"late_deceased\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with tcr_peripheral_a: 29 to 29 rows\n"
     ]
    }
   ],
   "source": [
    "df_clonality_benefit = cohort.as_dataframe(on=late_deceased)[[\"Clonality\", \"benefit\", \"late_deceased\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clonality</th>\n",
       "      <th>benefit</th>\n",
       "      <th>late_deceased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.149980</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.081610</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.145239</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.219364</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.146469</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.144153</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Clonality benefit late_deceased\n",
       "0    0.149980   False         False\n",
       "2    0.081610   False         False\n",
       "3    0.145239   False         False\n",
       "5    0.219364   False         False\n",
       "12   0.146469   False         False\n",
       "27   0.144153   False         False"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clonality_benefit[~df_clonality_benefit.late_deceased]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clonality        0.145854\n",
       "benefit          0.000000\n",
       "late_deceased    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clonality_benefit[~df_clonality_benefit.late_deceased].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dataframe_hash': -2899676230513618006,\n",
      " 'provenance_file_summary': {u'cohorts': u'0.4.0+3.gda968fb',\n",
      "                             u'isovar': u'0.0.6',\n",
      "                             u'mhctools': u'0.3.0',\n",
      "                             u'numpy': u'1.11.1',\n",
      "                             u'pandas': u'0.18.1',\n",
      "                             u'pyensembl': u'1.0.3',\n",
      "                             u'scipy': u'0.18.1',\n",
      "                             u'topiary': u'0.1.0',\n",
      "                             u'varcode': u'0.5.10'}}\n"
     ]
    }
   ],
   "source": [
    "cohort = data.init_cohort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with ensembl_coverage: 25 to 25 rows\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney test: U=33.0, p-value=0.262307331029 (two-sided)\n",
      "{{{late_deceased_missense_plot}}}\n",
      "{{{late_deceased_missense_late_deceased:0.98 (range 0.019-11.46)}}}\n",
      "{{{late_deceased_missense_no_late_deceased:4.24 (range 0.094-9.90)}}}\n",
      "{{{late_deceased_missense_mw:n=25, Mann-Whitney p=0.26}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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Qbdq0Yc2aNRpdLiLiYpaOMPr06WM/wf3nn1atWtG3b98iL2z//v3UqVOnQHvt2rU5ceJE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\nYokCQ0RELFFgiIiIJQoMERGx5P8AqQEUlmcM1HQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7facf08a1a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mann_whitney_hyper_label_printer(cohort.plot_boolean(on={\"Missense SNV Count / MB\": missense_snv_count,\n",
    "                                                         \"> 3 Months OS\": late_deceased},\n",
    "                                                     plot_col=\"Missense SNV Count / MB\", boolean_col=\"> 3 Months OS\",\n",
    "                                                     boolean_label=\"Overall Survival\",\n",
    "                                                     boolean_value_map={True: \"> 3 Months\", False: \"< 3 Months\"}),\n",
    "                                 \"late_deceased_missense\", split_col=\"late_deceased\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  },
  "name": "TCR Clonality.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
